Centre for Research into Ecological & Environmental Modelling (CREEM) Technical report serieshttp://hdl.handle.net/10023/6262017-08-18T03:07:27Z2017-08-18T03:07:27ZComparison of aerial survey methods for estimating abundance of common scotersRexstad, EricBuckland, Stephen T.http://hdl.handle.net/10023/7842016-03-28T11:00:31Z2009-01-01T00:00:00ZDuring the month of March, four survey methods were applied to the SPA at Camarthen Bay. WWT staff carried out visual aerial surveys using distance sampling methodology (Camphuysen et al. 2004). Visual shore-based counts were also conducted. Distance measures were not consistently taken by these observers, nor was survey effort equal among the four surveys. Because they are intended to be complete counts without replication within a day, it is not possible to estimate precision of these counts, or assess bias, making comparison with other survey results difficult. Digital still data were collected and processed by APEM Ltd. Digital video imagery were captured and processed by HiDef. This report revision includes 29 March survey data from HiDef not available at the time of the release of our 17 July report.
2009-01-01T00:00:00ZRexstad, EricBuckland, Stephen T.During the month of March, four survey methods were applied to the SPA at Camarthen Bay. WWT staff carried out visual aerial surveys using distance sampling methodology (Camphuysen et al. 2004). Visual shore-based counts were also conducted. Distance measures were not consistently taken by these observers, nor was survey effort equal among the four surveys. Because they are intended to be complete counts without replication within a day, it is not possible to estimate precision of these counts, or assess bias, making comparison with other survey results difficult. Digital still data were collected and processed by APEM Ltd. Digital video imagery were captured and processed by HiDef. This report revision includes 29 March survey data from HiDef not available at the time of the release of our 17 July report.Estimating the distribution of demersal fishing effort from VMS data using hidden Markov models.Borchers, David L.Reid, David G.http://hdl.handle.net/10023/6362016-03-28T10:48:08Z2008-01-01T00:00:00ZPreviously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000461/
2008-01-01T00:00:00ZBorchers, David L.Reid, David G.Incorporating Model Uncertainty into the Sequential Importance Sampling Framework using a Model Averaging Approach, or Trans-Dimensional Sequential Importance Sampling.Lynam, ChristopherKing, RuthThomas, LenBuckland, Stephen T.http://hdl.handle.net/10023/6352016-04-27T16:24:23Z2007-01-01T00:00:00ZA sequential Bayesian Monte Carlo approach is proposed in which model space can be explored during the Sequential Importance Sampling (SIS, a.k.a. Particle Filtering) fitting process. The algorithm allows model space to be explored while filtering forwards through time and takes a similar approach to Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategies, whereby parameters jump into and out of the model structure. Possible efficiency gains of the new Trans-Dimensional SIS routine are discussed and the approach is considered most beneficial when the exploration of large model space in the SIS framework is desired.
Previously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000463/
2007-01-01T00:00:00ZLynam, ChristopherKing, RuthThomas, LenBuckland, Stephen T.A sequential Bayesian Monte Carlo approach is proposed in which model space can be explored during the Sequential Importance Sampling (SIS, a.k.a. Particle Filtering) fitting process. The algorithm allows model space to be explored while filtering forwards through time and takes a similar approach to Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategies, whereby parameters jump into and out of the model structure. Possible efficiency gains of the new Trans-Dimensional SIS routine are discussed and the approach is considered most beneficial when the exploration of large model space in the SIS framework is desired.Accommodating availability bias on line transect surveys using hidden Markov models.Borchers, David L.Samara, Filipa I. P.http://hdl.handle.net/10023/6332016-03-28T10:49:25Z2007-01-01T00:00:00ZMaximum likelihood methods are developed which accommodate intermittent animal availability of animals on line transect surveys. Existing 'availability bias' correction methods are shown to be inadequate in general. The new method is applied to an aerial survey of whales, using a hidden Markov model to characterise the availability process.
Previously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000458/
2007-01-01T00:00:00ZBorchers, David L.Samara, Filipa I. P.Maximum likelihood methods are developed which accommodate intermittent animal availability of animals on line transect surveys. Existing 'availability bias' correction methods are shown to be inadequate in general. The new method is applied to an aerial survey of whales, using a hidden Markov model to characterise the availability process.Investigation of towed hydrophone monitoring power for harbour porpoise on the SCANS II survey.Borchers, David L.Burt, M. Louise.http://hdl.handle.net/10023/6322016-03-28T10:48:00Z2007-01-01T00:00:00ZWe investigate the power of harbour porpoise monitoring programmes which use an index of relative abundance to detect change. Power depends on the variability in the constant of proportionality relating the index to absolute abundance, as well as on the variability in the index given this constant. We estimate both from the SCANS II data and from European Seabirds at Sea (ESAS) data. Estimates of the coefficient of variation of the constant of proportionality are large and this results in very low power. Because these estimates may be unrealistically large for well-designed monitoring programs, we feel it is inappropriate to draw strong conclusions about the power of future monitoring programmes based on them.
ESAS surveys are found to be more efficient in terms of effort required to achieve given power, than the SCANS II passive acoustic surveys. However, the comparison may not be a fair one, for the following reason. The estimated CV of the constant of proportionality is obtained from the ratio of the index of density and the corresponding SCANS II absolute density estimate; the ESAS index is likely to be more highly correlated with the SCANS II estimate than the acoustic index, because like the SCANS II estimate, it is based on visual detections. In addition, standardization of the passive acoustic survey methods could yield substantially higher efficiency.
We provide a table giving power as a function of the CV of the constant of proportionality and the CV of the index, given this constant - this can be used to compare methods if reliable estimates of these CVs are available.
Previously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000457/
2007-01-01T00:00:00ZBorchers, David L.Burt, M. Louise.We investigate the power of harbour porpoise monitoring programmes which use an index of relative abundance to detect change. Power depends on the variability in the constant of proportionality relating the index to absolute abundance, as well as on the variability in the index given this constant. We estimate both from the SCANS II data and from European Seabirds at Sea (ESAS) data. Estimates of the coefficient of variation of the constant of proportionality are large and this results in very low power. Because these estimates may be unrealistically large for well-designed monitoring programs, we feel it is inappropriate to draw strong conclusions about the power of future monitoring programmes based on them.
ESAS surveys are found to be more efficient in terms of effort required to achieve given power, than the SCANS II passive acoustic surveys. However, the comparison may not be a fair one, for the following reason. The estimated CV of the constant of proportionality is obtained from the ratio of the index of density and the corresponding SCANS II absolute density estimate; the ESAS index is likely to be more highly correlated with the SCANS II estimate than the acoustic index, because like the SCANS II estimate, it is based on visual detections. In addition, standardization of the passive acoustic survey methods could yield substantially higher efficiency.
We provide a table giving power as a function of the CV of the constant of proportionality and the CV of the index, given this constant - this can be used to compare methods if reliable estimates of these CVs are available.Methods for estimating sperm whale abundance from passive acoustic line transect surveys.Borchers, David L.Brewer, CiaraMatthews, Justinhttp://hdl.handle.net/10023/6312016-03-28T10:48:18Z2007-01-01T00:00:00ZPreviously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000456/
2007-01-01T00:00:00ZBorchers, David L.Brewer, CiaraMatthews, JustinPoint and interval estimates of abundance using multiple covariate distance sampling: an example using great bustards.Rexstad, Erichttp://hdl.handle.net/10023/6292016-03-28T10:48:03Z2007-01-01T00:00:00ZDescription of computations to produce sex-specific estimates of density from a multiple-covariate distance sampling analysis. Program Distance 5.0 has limited capacity to bootstrap certain types of analytical situations (e.g., cluster size as a covariate). Herein I describe steps and code to perform an analysis of this sort. Possible ways to adapt this code for similar analyses are described.
Previously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000447/; The pdf file contains the tech report, the ASCII (.R) file contains the accompanying R code.
2007-01-01T00:00:00ZRexstad, EricDescription of computations to produce sex-specific estimates of density from a multiple-covariate distance sampling analysis. Program Distance 5.0 has limited capacity to bootstrap certain types of analytical situations (e.g., cluster size as a covariate). Herein I describe steps and code to perform an analysis of this sort. Possible ways to adapt this code for similar analyses are described.Non-uniform coverage estimators for distance sampling.Rexstad, Erichttp://hdl.handle.net/10023/6282016-03-28T10:48:04Z2007-01-01T00:00:00ZAllocation of sampling effort in the context of distance sampling is considered.
Specifically, allocation of effort in proportion to portions of the survey region that likely
contain high concentrations of animals are explored. The probability of a portion of the
survey region being included in the sample is proportional to the estimated number of
animals in that portion. These estimated numbers of animals may be derived from a
density surface model. This results in unequal coverage probability, and a Horvitz-
Thompson like estimator can be used to estimate population abundance. The properties
of this estimator is explored here via simulation. The benefits, measured in terms of
increased precision over traditional equal coverage probability estimators, are meagre,
and largely manifested when the underlying population distribution is a smooth gradient.
Previously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000445/
2007-01-01T00:00:00ZRexstad, EricAllocation of sampling effort in the context of distance sampling is considered.
Specifically, allocation of effort in proportion to portions of the survey region that likely
contain high concentrations of animals are explored. The probability of a portion of the
survey region being included in the sample is proportional to the estimated number of
animals in that portion. These estimated numbers of animals may be derived from a
density surface model. This results in unequal coverage probability, and a Horvitz-
Thompson like estimator can be used to estimate population abundance. The properties
of this estimator is explored here via simulation. The benefits, measured in terms of
increased precision over traditional equal coverage probability estimators, are meagre,
and largely manifested when the underlying population distribution is a smooth gradient.